A common feature of biochemical systems, especially those in which protein-protein interactions are prominent, is combinatorial complexity, which is present whenever a relatively small number of interactions have the potential to generate a much larger number of distinct chemical species and reactions. For a system marked by combinatorial complexity, the conventional approach of manually specifying each term of a mathematical model is impossible if the model is to account comprehensively for the consequences of interactions at the level of biomolecular sites. The primary goal of this proposal is to enable rapid development of mechanistic models of signal-transduction systems that account as completely as possible for the consequences of protein-protein interactions in a logically consistent way. To achieve this goal, we will develop tools for model specification and checking. These tools will be based on methods that involve the use of graphs to represent proteins and graph rewriting rules to represent protein-protein interactions. The rules are visual, much like diagrammatic interaction maps. Each rule specifies a type of binding/enzymatic reaction that arises from a biomolecular interaction and identifies features of reactants. Rules can be interpreted automatically, through procedures of graph rewriting, to obtain various types of mathematical models. Thus, rules enable precise and comprehensible visualization of biomolecular interactions. Importantly, a set of rules is compositional, in that each rule may be specified and refined independently. Equations in a conventional model on the other hand are typically interrelated, and changing an assumption about a protein-protein interaction may require numerous modifications of multiple equations. The second part of the proposed work is aimed at demonstrating the practicality of rule-based modeling. To ensure and demonstrate that our tools are useful, we will develop models for a number of biological systems. We will also demonstrate how our tools, together with database resources, can be used as part of a high- throughput modeling pipeline. An important capability we wish to achieve is the ability to model a significant fraction of the known human signal-transduction systems.